Twitter: Fast and Concise
On May 10, 2012, immediately after market closing, JPMorgan-Chase CEO Jamie Dimon held a shareholder call to announce a $2 billion trading loss. While traditional news agencies reported the call announcement late in the afternoon, Twitter led the way with reports from call participants who started tweeting while on the call a few minutes after it started.

To see how the volume on Twitter evolved, see figure 1. In each case, the points represent activity volumes on the topic of JPMorgan and “loss” while the lines represent function fits to either the Social Media Pulse or a Gaussian curve (a simple approximation for expected event traffic when averaging over the daily cycle.)

As Reuters and others released news stories and Europe started to wake up, a second Twitter pulse is visible. Toward the right-hand of the graph, the daily cycle of Tweets dominates the conversation about JPMorgan and “loss” with a curve more characteristic of broadly reported, expected events.

Figure 1. Twitter and Stocktwits audience comment on JPMorgan and “loss” after the announcement of a $2B trading loss on the evening May 10, 2012. Volumes are normalized so that peak volume = 1 for each publisher.

StockTwits: Fast and Concise, Focused

Much of the analysis that applies to Twitter applies to StockTwits–the major exceptions are in the expertise of the users and focus of the content. The StockTwits service serves traders and participants are mostly professional investors. Because the audience and the content is curated, there is very little off-topic chatter. Further, much of the content is specific analysis of JPMorgan’s loss, analysis of the stock price movement following the announcement and information about after-hours price indicators.
On Friday (May 11th), discussion of the loss reaches only about 40% of the peak of the night before. This is likely due to the message rapidly saturating the highly connected community on StockTwits.

Comments: Both Fast and Slow, Concise

Because there was a lot of financial news attention on the story, news stories started to appear soon after the call and these attracted comments immediately (this was the fast response). The data shown in Figure 2 includes both comments from Automattic and Disqus. These comment platforms are used for comments on both personal blogs and on news stories posted online by news organizations, so there is a mix of comments on news stories as well as personal analysis.

Figure 2. Commenters on blogs and news stories react to the announcement of ta $2B trading loss on the evening 10 May 2012, and an even stronger contingent react early on 11 May. Volumes are normalized so that peak volume = 1.

More-considered news and blog stories appeared on May 11th, Friday morning and these spurred a second (slower) pulse of comment responses.

An additional pattern that is often seen in comments is that people tend to read blogs at certain times of day (e.g. morning or evening) by habit. Because of this, we sometimes see comment volumes spiking at the start or end of the day in very active timezones.

Tumblr: Medium and Very Rich

The Tumblr audience reacted to the news as if the story was broken on Tumblr rather than broken on traditional news. This is unique among the publishers studied here. This pattern of slowly growing traffic during the first few hours after the shareholder call may indicate the nature of the conversation on Tumblr. Rather than an event-response reaction such as twitter, or a considered reaction, as with blogs, the reaction of the audience on Tumblr accelerates as the type of content Tumblrs reblog appears in the network. While the initial posts on Tumblr refer to news stories, the spread of the story through reblogging happens as a ramp up to the peak over a few hours.

The following day, the Tumblr story evolves like an expected event.

Not only is the timeline unique, but Tumblr content is also unique. Early posts have rich media including political cartoons and more right-brained political commentary and humor than the text-comment crowd. Adding Tumblr to your social media mix may present additional challenges in evaluating and analyzing the content, but the sensibilities as well as the activity of this audience adds a dimension not found in the content from the other publishers.

Blogs: Medium and Rich

A few quick, factual reports from the call were published in the form of blog posts as can be seen by the slight “heaviness” in the curve at the end of the day (May 10th). However, the large majority of the blog traffic is the traditional, considered and refined reactions published throughout the following day. The traffic on May 11th follows the pattern of an event everyone already knows about. The discussion here is analysis and commentary as people explore the implications of the story.

The large majority of the blog content is text or text with a picture of Mr. Dimon. Stories vary from dozens of words to a few thousand.

Figure 3. Content-rich and text-rich reactions to the announcement of ta $2B trading loss on the evening 10 May 2012. In-depth analysis continues with heavy posting during the day on the 11 May. Volumes are normalized so that peak volume = 1 for each publisher.

Finally, take a look at these timelines shown together in Figure 4. This view gives a clear indication of the timing of reactions between the publishers.

Figure 4. The points show the normalized volume of activities about “JPMorgan” and “loss” following the May 10th announcement from Jamie Dimon. Lines represent fits to models of typical social media reactions. Volumes are normalized so that peak volume = 1 for each publisher.

ConclusionThis example story demonstrates the potential of mixing perspectives, audience and styles of conversation in creating a full description of the social media response to events. With the right mix, we can identify stories and emerging topics within minutes and we can quickly characterize the relative size and speed of a story. We can identify user engagement, dig into deeper analysis, and the rate and focus of content sharing. With this mix of social data, we might be getting close to the perfect cocktail.